Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for pred...

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Autores principales:	Ahmad Chaddad, Jiali Li, Qizong Lu, Yujie Li, Idowu Paul Okuwobi, Camel Tanougast, Christian Desrosiers, Tamim Niazi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	AI radiomic autism deep learning MRI Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/e13821a8309948718b05e1a6a3a4c4ae
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id	oai:doaj.org-article:e13821a8309948718b05e1a6a3a4c4ae
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spelling	oai:doaj.org-article:e13821a8309948718b05e1a6a3a4c4ae2021-11-25T17:20:58ZCan Autism Be Diagnosed with Artificial Intelligence? A Narrative Review10.3390/diagnostics111120322075-4418https://doaj.org/article/e13821a8309948718b05e1a6a3a4c4ae2021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/2032https://doaj.org/toc/2075-4418Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.Ahmad ChaddadJiali LiQizong LuYujie LiIdowu Paul OkuwobiCamel TanougastChristian DesrosiersTamim NiaziMDPI AGarticleAIradiomicautismdeep learningMRIMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 2032, p 2032 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	AI radiomic autism deep learning MRI Medicine (General) R5-920
spellingShingle	AI radiomic autism deep learning MRI Medicine (General) R5-920 Ahmad Chaddad Jiali Li Qizong Lu Yujie Li Idowu Paul Okuwobi Camel Tanougast Christian Desrosiers Tamim Niazi Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review
description	Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.
format	article
author	Ahmad Chaddad Jiali Li Qizong Lu Yujie Li Idowu Paul Okuwobi Camel Tanougast Christian Desrosiers Tamim Niazi
author_facet	Ahmad Chaddad Jiali Li Qizong Lu Yujie Li Idowu Paul Okuwobi Camel Tanougast Christian Desrosiers Tamim Niazi
author_sort	Ahmad Chaddad
title	Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review
title_short	Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review
title_full	Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review
title_fullStr	Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review
title_full_unstemmed	Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review
title_sort	can autism be diagnosed with artificial intelligence? a narrative review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e13821a8309948718b05e1a6a3a4c4ae
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Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review

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